{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auth success \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('max_row',300) \n",
    "import os\n",
    "import numpy as np\n",
    "import tushare as ts\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../utils/\")\n",
    "import date_util\n",
    "import enter_util\n",
    "import plot_util\n",
    "import token_util\n",
    "import data_util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 判断原则：达到要求后选vol_mean_20最大的，ST股票不买\n",
    "##### 若不是选最大的，则查看其前段时间的增长速率，以及eda,dif的高度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算当天可购买的原始数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. dea - macd < 0.2\n",
    "2. macd 小于最近两个月的macd最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "concept_list = ['GN201', 'GN162', 'GN1050', 'GN228', 'GN1021', 'GN210', 'GN215', 'GN665']\n",
    "industry_list = ['801085', '801156', '801124']\n",
    "\n",
    "stock_list = data_util.get_industry_concept(industry_list, concept_list)\n",
    "stock_list = list(set(stock_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个月内有上涨趋势就行(周k)。日k不高开低走，且dif,dea与macd差值较小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = \"20191010\"\n",
    "date, raw_choose = enter_util.get_weekk_condidate_stock(date, stock_list_root=stock_list)\n",
    "choose_list = enter_util.get_condidate_stock(date, raw_choose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_day</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2737</td>\n",
       "      <td>002241.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>0.961791</td>\n",
       "      <td>-2.039131</td>\n",
       "      <td>3.272353</td>\n",
       "      <td>0.255172</td>\n",
       "      <td>0.586216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>514</td>\n",
       "      <td>603127.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>2.431093</td>\n",
       "      <td>-0.568251</td>\n",
       "      <td>-0.627702</td>\n",
       "      <td>0.373314</td>\n",
       "      <td>0.564800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2886</td>\n",
       "      <td>002152.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.521237</td>\n",
       "      <td>-1.165770</td>\n",
       "      <td>1.658684</td>\n",
       "      <td>2.208424</td>\n",
       "      <td>0.383897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2294</td>\n",
       "      <td>002352.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>0.695455</td>\n",
       "      <td>0.772025</td>\n",
       "      <td>-0.208443</td>\n",
       "      <td>-0.121290</td>\n",
       "      <td>0.297095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3513</td>\n",
       "      <td>600986.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.762643</td>\n",
       "      <td>0.745203</td>\n",
       "      <td>0.529304</td>\n",
       "      <td>1.225496</td>\n",
       "      <td>0.271208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>504</td>\n",
       "      <td>603882.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>2.094997</td>\n",
       "      <td>-0.986803</td>\n",
       "      <td>-0.538917</td>\n",
       "      <td>-0.640873</td>\n",
       "      <td>0.195181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3138</td>\n",
       "      <td>002044.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.394986</td>\n",
       "      <td>0.971300</td>\n",
       "      <td>0.220208</td>\n",
       "      <td>0.070666</td>\n",
       "      <td>0.133939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>924</td>\n",
       "      <td>603866.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>0.195245</td>\n",
       "      <td>0.523147</td>\n",
       "      <td>-0.644165</td>\n",
       "      <td>0.447134</td>\n",
       "      <td>0.123797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2057</td>\n",
       "      <td>601799.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>1.141755</td>\n",
       "      <td>0.845730</td>\n",
       "      <td>-0.654017</td>\n",
       "      <td>-1.799764</td>\n",
       "      <td>0.020916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>117</td>\n",
       "      <td>603317.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.075211</td>\n",
       "      <td>-0.126221</td>\n",
       "      <td>-0.575103</td>\n",
       "      <td>0.869075</td>\n",
       "      <td>0.010987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3982</td>\n",
       "      <td>600203.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.705680</td>\n",
       "      <td>0.086678</td>\n",
       "      <td>-0.228361</td>\n",
       "      <td>0.898642</td>\n",
       "      <td>-0.060312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1135</td>\n",
       "      <td>603788.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.636475</td>\n",
       "      <td>0.998309</td>\n",
       "      <td>-0.666954</td>\n",
       "      <td>-0.095778</td>\n",
       "      <td>-0.143827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1630</td>\n",
       "      <td>002464.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.314526</td>\n",
       "      <td>-1.983029</td>\n",
       "      <td>0.797871</td>\n",
       "      <td>0.863346</td>\n",
       "      <td>-0.158720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1948</td>\n",
       "      <td>002570.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.758005</td>\n",
       "      <td>1.105587</td>\n",
       "      <td>-0.375702</td>\n",
       "      <td>-0.666973</td>\n",
       "      <td>-0.214819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2317</td>\n",
       "      <td>002351.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.434203</td>\n",
       "      <td>-0.958732</td>\n",
       "      <td>-0.223435</td>\n",
       "      <td>0.176620</td>\n",
       "      <td>-0.331370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1707</td>\n",
       "      <td>002695.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.782407</td>\n",
       "      <td>0.451659</td>\n",
       "      <td>-0.536821</td>\n",
       "      <td>-0.539736</td>\n",
       "      <td>-0.359702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3982</td>\n",
       "      <td>000895.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.770799</td>\n",
       "      <td>0.678660</td>\n",
       "      <td>-0.017936</td>\n",
       "      <td>-1.545123</td>\n",
       "      <td>-0.408120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3979</td>\n",
       "      <td>600763.SH</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.552679</td>\n",
       "      <td>0.066970</td>\n",
       "      <td>-0.598527</td>\n",
       "      <td>-0.746318</td>\n",
       "      <td>-0.421379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2083</td>\n",
       "      <td>002452.SZ</td>\n",
       "      <td>20191010</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.811485</td>\n",
       "      <td>0.582668</td>\n",
       "      <td>-0.582338</td>\n",
       "      <td>-1.232031</td>\n",
       "      <td>-0.489786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date  name   dif_dea  dif_dea_day  vol_mean_20  \\\n",
       "0   2737  002241.SZ   20191010  None  0.961791    -2.039131     3.272353   \n",
       "1    514  603127.SH   20191010  None  2.431093    -0.568251    -0.627702   \n",
       "2   2886  002152.SZ   20191010  None -0.521237    -1.165770     1.658684   \n",
       "3   2294  002352.SZ   20191010  None  0.695455     0.772025    -0.208443   \n",
       "4   3513  600986.SH   20191010  None -0.762643     0.745203     0.529304   \n",
       "5    504  603882.SH   20191010  None  2.094997    -0.986803    -0.538917   \n",
       "6   3138  002044.SZ   20191010  None -0.394986     0.971300     0.220208   \n",
       "7    924  603866.SH   20191010  None  0.195245     0.523147    -0.644165   \n",
       "8   2057  601799.SH   20191010  None  1.141755     0.845730    -0.654017   \n",
       "9    117  603317.SH   20191010  None -0.075211    -0.126221    -0.575103   \n",
       "10  3982  600203.SH   20191010  None -0.705680     0.086678    -0.228361   \n",
       "11  1135  603788.SH   20191010  None -0.636475     0.998309    -0.666954   \n",
       "12  1630  002464.SZ   20191010  None -0.314526    -1.983029     0.797871   \n",
       "13  1948  002570.SZ   20191010  None -0.758005     1.105587    -0.375702   \n",
       "14  2317  002351.SZ   20191010  None -0.434203    -0.958732    -0.223435   \n",
       "15  1707  002695.SZ   20191010  None -0.782407     0.451659    -0.536821   \n",
       "16  3982  000895.SZ   20191010  None -0.770799     0.678660    -0.017936   \n",
       "17  3979  600763.SH   20191010  None -0.552679     0.066970    -0.598527   \n",
       "18  2083  002452.SZ   20191010  None -0.811485     0.582668    -0.582338   \n",
       "\n",
       "    vol_discount_mean  rank_factor  \n",
       "0            0.255172     0.586216  \n",
       "1            0.373314     0.564800  \n",
       "2            2.208424     0.383897  \n",
       "3           -0.121290     0.297095  \n",
       "4            1.225496     0.271208  \n",
       "5           -0.640873     0.195181  \n",
       "6            0.070666     0.133939  \n",
       "7            0.447134     0.123797  \n",
       "8           -1.799764     0.020916  \n",
       "9            0.869075     0.010987  \n",
       "10           0.898642    -0.060312  \n",
       "11          -0.095778    -0.143827  \n",
       "12           0.863346    -0.158720  \n",
       "13          -0.666973    -0.214819  \n",
       "14           0.176620    -0.331370  \n",
       "15          -0.539736    -0.359702  \n",
       "16          -1.545123    -0.408120  \n",
       "17          -0.746318    -0.421379  \n",
       "18          -1.232031    -0.489786  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choose_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x540 with 19 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "choose_data_plot = enter_util.enter_for_plot(choose_list)\n",
    "enter_true, enter_false, enter_discount = plot_util.plot_enter(choose_data_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = \"20191104\"\n",
    "date, raw_choose = enter_util.get_weekk_condidate_stock(date)\n",
    "choose_list = enter_util.get_condidate_stock(date, raw_choose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "choose_data_plot = enter_util.enter_for_plot(choose_list)\n",
    "enter_true, enter_false, enter_discount = plot_util.plot_enter(choose_data_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = \"20191014\"\n",
    "date, raw_choose = enter_util.get_weekk_condidate_stock(date)\n",
    "choose_list = enter_util.get_condidate_stock(date, raw_choose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_day</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>349</td>\n",
       "      <td>603259.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>药明康德</td>\n",
       "      <td>4.795045</td>\n",
       "      <td>-0.301175</td>\n",
       "      <td>-0.239541</td>\n",
       "      <td>0.186589</td>\n",
       "      <td>1.367688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3984</td>\n",
       "      <td>000636.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>风华高科</td>\n",
       "      <td>0.559020</td>\n",
       "      <td>-0.563463</td>\n",
       "      <td>3.602630</td>\n",
       "      <td>0.320302</td>\n",
       "      <td>0.839600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1666</td>\n",
       "      <td>002660.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>茂硕电源</td>\n",
       "      <td>-0.334617</td>\n",
       "      <td>-0.797972</td>\n",
       "      <td>0.825261</td>\n",
       "      <td>2.583793</td>\n",
       "      <td>0.421831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3984</td>\n",
       "      <td>000861.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>海印股份</td>\n",
       "      <td>-0.687058</td>\n",
       "      <td>0.734418</td>\n",
       "      <td>-0.025346</td>\n",
       "      <td>1.180541</td>\n",
       "      <td>0.171805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1632</td>\n",
       "      <td>002464.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>众应互联</td>\n",
       "      <td>0.049979</td>\n",
       "      <td>-2.815743</td>\n",
       "      <td>2.490877</td>\n",
       "      <td>1.089235</td>\n",
       "      <td>0.167868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3783</td>\n",
       "      <td>600552.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>凯盛科技</td>\n",
       "      <td>-0.562663</td>\n",
       "      <td>0.389501</td>\n",
       "      <td>0.710661</td>\n",
       "      <td>0.476383</td>\n",
       "      <td>0.146510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1970</td>\n",
       "      <td>601567.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>三星医疗</td>\n",
       "      <td>-0.313388</td>\n",
       "      <td>-0.456794</td>\n",
       "      <td>-0.331262</td>\n",
       "      <td>1.830771</td>\n",
       "      <td>0.114527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3984</td>\n",
       "      <td>600201.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>生物股份</td>\n",
       "      <td>0.363941</td>\n",
       "      <td>-0.248085</td>\n",
       "      <td>1.082162</td>\n",
       "      <td>-0.819403</td>\n",
       "      <td>0.112117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>576</td>\n",
       "      <td>603042.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>华脉科技</td>\n",
       "      <td>0.276933</td>\n",
       "      <td>-1.104669</td>\n",
       "      <td>-0.137936</td>\n",
       "      <td>1.330133</td>\n",
       "      <td>0.100586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3547</td>\n",
       "      <td>002019.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>亿帆医药</td>\n",
       "      <td>-0.086968</td>\n",
       "      <td>0.379540</td>\n",
       "      <td>0.085773</td>\n",
       "      <td>-0.007321</td>\n",
       "      <td>0.065508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>553</td>\n",
       "      <td>603331.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>百达精工</td>\n",
       "      <td>-0.661311</td>\n",
       "      <td>1.232919</td>\n",
       "      <td>-0.917647</td>\n",
       "      <td>0.914671</td>\n",
       "      <td>0.047595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3981</td>\n",
       "      <td>600211.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>西藏药业</td>\n",
       "      <td>0.403153</td>\n",
       "      <td>1.119028</td>\n",
       "      <td>-0.835426</td>\n",
       "      <td>-0.804756</td>\n",
       "      <td>0.016715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1954</td>\n",
       "      <td>601677.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>明泰铝业</td>\n",
       "      <td>-0.602737</td>\n",
       "      <td>1.423668</td>\n",
       "      <td>-0.757192</td>\n",
       "      <td>0.295264</td>\n",
       "      <td>0.011527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2914</td>\n",
       "      <td>601808.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>中海油服</td>\n",
       "      <td>0.060547</td>\n",
       "      <td>-0.075560</td>\n",
       "      <td>0.109586</td>\n",
       "      <td>-0.389380</td>\n",
       "      <td>-0.052907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3984</td>\n",
       "      <td>600704.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>物产中大</td>\n",
       "      <td>-0.707445</td>\n",
       "      <td>1.208054</td>\n",
       "      <td>0.063772</td>\n",
       "      <td>-0.517196</td>\n",
       "      <td>-0.061308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2303</td>\n",
       "      <td>601158.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>重庆水务</td>\n",
       "      <td>-0.708961</td>\n",
       "      <td>1.502826</td>\n",
       "      <td>-0.809326</td>\n",
       "      <td>-0.014926</td>\n",
       "      <td>-0.076974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3984</td>\n",
       "      <td>000601.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>韶能股份</td>\n",
       "      <td>-0.458053</td>\n",
       "      <td>0.198637</td>\n",
       "      <td>-0.105576</td>\n",
       "      <td>0.177142</td>\n",
       "      <td>-0.083375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2341</td>\n",
       "      <td>002332.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>仙琚制药</td>\n",
       "      <td>-0.432790</td>\n",
       "      <td>0.982063</td>\n",
       "      <td>-0.166971</td>\n",
       "      <td>-0.619813</td>\n",
       "      <td>-0.090781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>549</td>\n",
       "      <td>603757.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>大元泵业</td>\n",
       "      <td>0.012561</td>\n",
       "      <td>0.230879</td>\n",
       "      <td>-0.681759</td>\n",
       "      <td>-0.033713</td>\n",
       "      <td>-0.093150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>712</td>\n",
       "      <td>603060.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>国检集团</td>\n",
       "      <td>0.305426</td>\n",
       "      <td>-0.978024</td>\n",
       "      <td>-0.785367</td>\n",
       "      <td>0.625452</td>\n",
       "      <td>-0.135960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>486</td>\n",
       "      <td>603110.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>东方材料</td>\n",
       "      <td>-0.239971</td>\n",
       "      <td>-0.428061</td>\n",
       "      <td>-0.374251</td>\n",
       "      <td>0.402185</td>\n",
       "      <td>-0.152017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3984</td>\n",
       "      <td>600261.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>阳光照明</td>\n",
       "      <td>-0.613134</td>\n",
       "      <td>0.409777</td>\n",
       "      <td>0.390886</td>\n",
       "      <td>-0.700362</td>\n",
       "      <td>-0.163880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>489</td>\n",
       "      <td>002902.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>铭普光磁</td>\n",
       "      <td>-0.110551</td>\n",
       "      <td>-0.394964</td>\n",
       "      <td>0.446948</td>\n",
       "      <td>-0.747252</td>\n",
       "      <td>-0.172219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>619</td>\n",
       "      <td>603178.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>圣龙股份</td>\n",
       "      <td>-0.000960</td>\n",
       "      <td>-0.222515</td>\n",
       "      <td>-0.777527</td>\n",
       "      <td>-0.065286</td>\n",
       "      <td>-0.213353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3984</td>\n",
       "      <td>600857.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>宁波中百</td>\n",
       "      <td>-0.665435</td>\n",
       "      <td>1.157578</td>\n",
       "      <td>-0.869160</td>\n",
       "      <td>-0.420658</td>\n",
       "      <td>-0.226078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1820</td>\n",
       "      <td>601238.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>广汽集团</td>\n",
       "      <td>-0.253992</td>\n",
       "      <td>0.218197</td>\n",
       "      <td>0.057876</td>\n",
       "      <td>-1.344132</td>\n",
       "      <td>-0.289809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1754</td>\n",
       "      <td>002473.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>圣莱达</td>\n",
       "      <td>-0.466506</td>\n",
       "      <td>0.174745</td>\n",
       "      <td>-0.796051</td>\n",
       "      <td>-0.361823</td>\n",
       "      <td>-0.336578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>607</td>\n",
       "      <td>002861.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>瀛通通讯</td>\n",
       "      <td>0.642475</td>\n",
       "      <td>-0.454152</td>\n",
       "      <td>-0.778747</td>\n",
       "      <td>-1.660118</td>\n",
       "      <td>-0.385861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1325</td>\n",
       "      <td>603308.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>应流股份</td>\n",
       "      <td>0.629446</td>\n",
       "      <td>-2.210756</td>\n",
       "      <td>0.035363</td>\n",
       "      <td>-0.963442</td>\n",
       "      <td>-0.438933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3984</td>\n",
       "      <td>600129.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>太极集团</td>\n",
       "      <td>-0.191987</td>\n",
       "      <td>-0.309895</td>\n",
       "      <td>-0.512710</td>\n",
       "      <td>-1.942880</td>\n",
       "      <td>-0.610693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date  name   dif_dea  dif_dea_day  vol_mean_20  \\\n",
       "0    349  603259.SH   20191014  药明康德  4.795045    -0.301175    -0.239541   \n",
       "1   3984  000636.SZ   20191014  风华高科  0.559020    -0.563463     3.602630   \n",
       "2   1666  002660.SZ   20191014  茂硕电源 -0.334617    -0.797972     0.825261   \n",
       "3   3984  000861.SZ   20191014  海印股份 -0.687058     0.734418    -0.025346   \n",
       "4   1632  002464.SZ   20191014  众应互联  0.049979    -2.815743     2.490877   \n",
       "5   3783  600552.SH   20191014  凯盛科技 -0.562663     0.389501     0.710661   \n",
       "6   1970  601567.SH   20191014  三星医疗 -0.313388    -0.456794    -0.331262   \n",
       "7   3984  600201.SH   20191014  生物股份  0.363941    -0.248085     1.082162   \n",
       "8    576  603042.SH   20191014  华脉科技  0.276933    -1.104669    -0.137936   \n",
       "9   3547  002019.SZ   20191014  亿帆医药 -0.086968     0.379540     0.085773   \n",
       "10   553  603331.SH   20191014  百达精工 -0.661311     1.232919    -0.917647   \n",
       "11  3981  600211.SH   20191014  西藏药业  0.403153     1.119028    -0.835426   \n",
       "12  1954  601677.SH   20191014  明泰铝业 -0.602737     1.423668    -0.757192   \n",
       "13  2914  601808.SH   20191014  中海油服  0.060547    -0.075560     0.109586   \n",
       "14  3984  600704.SH   20191014  物产中大 -0.707445     1.208054     0.063772   \n",
       "15  2303  601158.SH   20191014  重庆水务 -0.708961     1.502826    -0.809326   \n",
       "16  3984  000601.SZ   20191014  韶能股份 -0.458053     0.198637    -0.105576   \n",
       "17  2341  002332.SZ   20191014  仙琚制药 -0.432790     0.982063    -0.166971   \n",
       "18   549  603757.SH   20191014  大元泵业  0.012561     0.230879    -0.681759   \n",
       "19   712  603060.SH   20191014  国检集团  0.305426    -0.978024    -0.785367   \n",
       "20   486  603110.SH   20191014  东方材料 -0.239971    -0.428061    -0.374251   \n",
       "21  3984  600261.SH   20191014  阳光照明 -0.613134     0.409777     0.390886   \n",
       "22   489  002902.SZ   20191014  铭普光磁 -0.110551    -0.394964     0.446948   \n",
       "23   619  603178.SH   20191014  圣龙股份 -0.000960    -0.222515    -0.777527   \n",
       "24  3984  600857.SH   20191014  宁波中百 -0.665435     1.157578    -0.869160   \n",
       "25  1820  601238.SH   20191014  广汽集团 -0.253992     0.218197     0.057876   \n",
       "26  1754  002473.SZ   20191014   圣莱达 -0.466506     0.174745    -0.796051   \n",
       "27   607  002861.SZ   20191014  瀛通通讯  0.642475    -0.454152    -0.778747   \n",
       "28  1325  603308.SH   20191014  应流股份  0.629446    -2.210756     0.035363   \n",
       "29  3984  600129.SH   20191014  太极集团 -0.191987    -0.309895    -0.512710   \n",
       "\n",
       "    vol_discount_mean  rank_factor  \n",
       "0            0.186589     1.367688  \n",
       "1            0.320302     0.839600  \n",
       "2            2.583793     0.421831  \n",
       "3            1.180541     0.171805  \n",
       "4            1.089235     0.167868  \n",
       "5            0.476383     0.146510  \n",
       "6            1.830771     0.114527  \n",
       "7           -0.819403     0.112117  \n",
       "8            1.330133     0.100586  \n",
       "9           -0.007321     0.065508  \n",
       "10           0.914671     0.047595  \n",
       "11          -0.804756     0.016715  \n",
       "12           0.295264     0.011527  \n",
       "13          -0.389380    -0.052907  \n",
       "14          -0.517196    -0.061308  \n",
       "15          -0.014926    -0.076974  \n",
       "16           0.177142    -0.083375  \n",
       "17          -0.619813    -0.090781  \n",
       "18          -0.033713    -0.093150  \n",
       "19           0.625452    -0.135960  \n",
       "20           0.402185    -0.152017  \n",
       "21          -0.700362    -0.163880  \n",
       "22          -0.747252    -0.172219  \n",
       "23          -0.065286    -0.213353  \n",
       "24          -0.420658    -0.226078  \n",
       "25          -1.344132    -0.289809  \n",
       "26          -0.361823    -0.336578  \n",
       "27          -1.660118    -0.385861  \n",
       "28          -0.963442    -0.438933  \n",
       "29          -1.942880    -0.610693  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choose_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在计算时已取反，故为大于\n",
    "min_index = choose_list[\"dif_dea_day\"].idxmin()\n",
    "test1 = choose_list.drop([min_index], axis=0)\n",
    "test1 = test1[test1[\"dif_dea_day\"] > test1[\"dif_dea_day\"].mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_day</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3981</td>\n",
       "      <td>600606.SH</td>\n",
       "      <td>20191009</td>\n",
       "      <td>绿地控股</td>\n",
       "      <td>-0.796521</td>\n",
       "      <td>1.248763</td>\n",
       "      <td>2.158524</td>\n",
       "      <td>0.021859</td>\n",
       "      <td>0.446873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3155</td>\n",
       "      <td>002058.SZ</td>\n",
       "      <td>20191009</td>\n",
       "      <td>威尔泰</td>\n",
       "      <td>-0.444003</td>\n",
       "      <td>0.306885</td>\n",
       "      <td>-0.857226</td>\n",
       "      <td>1.679981</td>\n",
       "      <td>0.092727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1087</td>\n",
       "      <td>002730.SZ</td>\n",
       "      <td>20191009</td>\n",
       "      <td>电光科技</td>\n",
       "      <td>-0.561798</td>\n",
       "      <td>0.452911</td>\n",
       "      <td>-0.231078</td>\n",
       "      <td>-0.688123</td>\n",
       "      <td>-0.261797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>661</td>\n",
       "      <td>603038.SH</td>\n",
       "      <td>20191009</td>\n",
       "      <td>华立股份</td>\n",
       "      <td>-0.567459</td>\n",
       "      <td>0.828105</td>\n",
       "      <td>-0.908889</td>\n",
       "      <td>-0.549986</td>\n",
       "      <td>-0.296391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>514</td>\n",
       "      <td>603181.SH</td>\n",
       "      <td>20191009</td>\n",
       "      <td>皇马科技</td>\n",
       "      <td>-0.683970</td>\n",
       "      <td>0.998456</td>\n",
       "      <td>-0.598613</td>\n",
       "      <td>-1.214582</td>\n",
       "      <td>-0.368139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>498</td>\n",
       "      <td>603321.SH</td>\n",
       "      <td>20191009</td>\n",
       "      <td>梅轮电梯</td>\n",
       "      <td>-0.545173</td>\n",
       "      <td>0.288617</td>\n",
       "      <td>-0.619991</td>\n",
       "      <td>-1.093405</td>\n",
       "      <td>-0.448508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date  name   dif_dea  dif_dea_day  vol_mean_20  \\\n",
       "1   3981  600606.SH   20191009  绿地控股 -0.796521     1.248763     2.158524   \n",
       "5   3155  002058.SZ   20191009   威尔泰 -0.444003     0.306885    -0.857226   \n",
       "8   1087  002730.SZ   20191009  电光科技 -0.561798     0.452911    -0.231078   \n",
       "9    661  603038.SH   20191009  华立股份 -0.567459     0.828105    -0.908889   \n",
       "10   514  603181.SH   20191009  皇马科技 -0.683970     0.998456    -0.598613   \n",
       "11   498  603321.SH   20191009  梅轮电梯 -0.545173     0.288617    -0.619991   \n",
       "\n",
       "    vol_discount_mean  rank_factor  \n",
       "1            0.021859     0.446873  \n",
       "5            1.679981     0.092727  \n",
       "8           -0.688123    -0.261797  \n",
       "9           -0.549986    -0.296391  \n",
       "10          -1.214582    -0.368139  \n",
       "11          -1.093405    -0.448508  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1 # len(test1)>8进行下一步 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_index = test1[\"dif_dea\"].idxmin()\n",
    "test3 = test1.drop([min_index], axis=0)\n",
    "test3= test3[test3[\"dif_dea\"] > test3[\"dif_dea\"].mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_day</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>665</td>\n",
       "      <td>603040.SH</td>\n",
       "      <td>20191101</td>\n",
       "      <td>新坐标</td>\n",
       "      <td>0.368409</td>\n",
       "      <td>0.692597</td>\n",
       "      <td>-0.719945</td>\n",
       "      <td>1.334460</td>\n",
       "      <td>0.371945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>867</td>\n",
       "      <td>603919.SH</td>\n",
       "      <td>20191101</td>\n",
       "      <td>金徽酒</td>\n",
       "      <td>-0.161873</td>\n",
       "      <td>0.449727</td>\n",
       "      <td>-0.445705</td>\n",
       "      <td>-0.671807</td>\n",
       "      <td>-0.182119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date name   dif_dea  dif_dea_day  vol_mean_20  \\\n",
       "2    665  603040.SH   20191101  新坐标  0.368409     0.692597    -0.719945   \n",
       "10   867  603919.SH   20191101  金徽酒 -0.161873     0.449727    -0.445705   \n",
       "\n",
       "    vol_discount_mean  rank_factor  \n",
       "2            1.334460     0.371945  \n",
       "10          -0.671807    -0.182119  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test3 # len(test3) <= len(test2) / 2 返回上一步"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_index = test1[\"dif_dea_day\"].idxmin()\n",
    "# max_index = test1[\"dif_dea_day\"].idxmax()\n",
    "test2 = test1.drop([min_index], axis=0)\n",
    "test2 = test2[test2[\"dif_dea_day\"] > test2[\"dif_dea_day\"].mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_day</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3984</td>\n",
       "      <td>000513.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>丽珠集团</td>\n",
       "      <td>-0.360409</td>\n",
       "      <td>1.511514</td>\n",
       "      <td>0.006394</td>\n",
       "      <td>-0.412238</td>\n",
       "      <td>0.113011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2303</td>\n",
       "      <td>601158.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>重庆水务</td>\n",
       "      <td>-0.618273</td>\n",
       "      <td>1.189268</td>\n",
       "      <td>-0.705426</td>\n",
       "      <td>0.259034</td>\n",
       "      <td>-0.036907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3984</td>\n",
       "      <td>000153.SZ</td>\n",
       "      <td>20191014</td>\n",
       "      <td>丰原药业</td>\n",
       "      <td>-0.600807</td>\n",
       "      <td>1.170344</td>\n",
       "      <td>-0.670044</td>\n",
       "      <td>-0.752994</td>\n",
       "      <td>-0.230781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>581</td>\n",
       "      <td>603855.SH</td>\n",
       "      <td>20191014</td>\n",
       "      <td>华荣股份</td>\n",
       "      <td>-0.625325</td>\n",
       "      <td>1.496446</td>\n",
       "      <td>-0.780362</td>\n",
       "      <td>-1.302547</td>\n",
       "      <td>-0.304890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date  name   dif_dea  dif_dea_day  vol_mean_20  \\\n",
       "4   3984  000513.SZ   20191014  丽珠集团 -0.360409     1.511514     0.006394   \n",
       "11  2303  601158.SH   20191014  重庆水务 -0.618273     1.189268    -0.705426   \n",
       "18  3984  000153.SZ   20191014  丰原药业 -0.600807     1.170344    -0.670044   \n",
       "20   581  603855.SH   20191014  华荣股份 -0.625325     1.496446    -0.780362   \n",
       "\n",
       "    vol_discount_mean  rank_factor  \n",
       "4           -0.412238     0.113011  \n",
       "11           0.259034    -0.036907  \n",
       "18          -0.752994    -0.230781  \n",
       "20          -1.302547    -0.304890  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2 # len(test2)>=8进行下一步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "重点关注test4，但也不忽视test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_index = test2[\"vol_mean_20\"].idxmin()\n",
    "test4 = test2.drop([min_index], axis=0)\n",
    "test4= test4[test4[\"vol_mean_20\"] > test4[\"vol_mean_20\"].mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_day</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1800</td>\n",
       "      <td>002657.SZ</td>\n",
       "      <td>20191105</td>\n",
       "      <td>中科金财</td>\n",
       "      <td>-0.681083</td>\n",
       "      <td>1.015753</td>\n",
       "      <td>-0.281779</td>\n",
       "      <td>0.881191</td>\n",
       "      <td>0.118708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4000</td>\n",
       "      <td>600869.SH</td>\n",
       "      <td>20191105</td>\n",
       "      <td>智慧能源</td>\n",
       "      <td>-0.621769</td>\n",
       "      <td>0.766664</td>\n",
       "      <td>0.115307</td>\n",
       "      <td>-1.440493</td>\n",
       "      <td>-0.298235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date  name   dif_dea  dif_dea_day  vol_mean_20  \\\n",
       "8   1800  002657.SZ   20191105  中科金财 -0.681083     1.015753    -0.281779   \n",
       "31  4000  600869.SH   20191105  智慧能源 -0.621769     0.766664     0.115307   \n",
       "\n",
       "    vol_discount_mean  rank_factor  \n",
       "8            0.881191     0.118708  \n",
       "31          -1.440493    -0.298235  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test4 # len(test4) <= len(test2) / 2 返回上一步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对周k值进行筛选，剔除月k增长过慢的数据(0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dif_dea = choose_list[choose_list[\"dif_dea\"] > 0.05 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_0</th>\n",
       "      <th>revenue_mean</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3960</td>\n",
       "      <td>000519.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>中兵红箭</td>\n",
       "      <td>0.096063</td>\n",
       "      <td>-0.003129</td>\n",
       "      <td>0.708039</td>\n",
       "      <td>495468.7315</td>\n",
       "      <td>1.748179</td>\n",
       "      <td>99094.230960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2890</td>\n",
       "      <td>601808.SH</td>\n",
       "      <td>20190902</td>\n",
       "      <td>中海油服</td>\n",
       "      <td>0.159121</td>\n",
       "      <td>-0.004853</td>\n",
       "      <td>0.786317</td>\n",
       "      <td>102085.7880</td>\n",
       "      <td>1.528857</td>\n",
       "      <td>20417.629055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3960</td>\n",
       "      <td>000950.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>重药控股</td>\n",
       "      <td>0.053289</td>\n",
       "      <td>-0.005262</td>\n",
       "      <td>0.688062</td>\n",
       "      <td>91999.8085</td>\n",
       "      <td>1.667091</td>\n",
       "      <td>18400.414314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3434</td>\n",
       "      <td>002025.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>航天电器</td>\n",
       "      <td>0.346147</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>0.827409</td>\n",
       "      <td>70923.0915</td>\n",
       "      <td>0.968324</td>\n",
       "      <td>14185.039920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2421</td>\n",
       "      <td>002294.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>信立泰</td>\n",
       "      <td>0.065282</td>\n",
       "      <td>-0.002448</td>\n",
       "      <td>0.658768</td>\n",
       "      <td>42178.3145</td>\n",
       "      <td>1.370126</td>\n",
       "      <td>8436.055325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3960</td>\n",
       "      <td>001872.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>招商港口</td>\n",
       "      <td>0.139084</td>\n",
       "      <td>0.009176</td>\n",
       "      <td>0.733599</td>\n",
       "      <td>34805.2205</td>\n",
       "      <td>1.381836</td>\n",
       "      <td>6961.472232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1024</td>\n",
       "      <td>002755.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>奥赛康</td>\n",
       "      <td>0.056974</td>\n",
       "      <td>-0.010211</td>\n",
       "      <td>0.695590</td>\n",
       "      <td>33454.3490</td>\n",
       "      <td>1.420239</td>\n",
       "      <td>6691.275278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>781</td>\n",
       "      <td>002801.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>微光股份</td>\n",
       "      <td>0.357187</td>\n",
       "      <td>0.030788</td>\n",
       "      <td>0.790432</td>\n",
       "      <td>19549.0750</td>\n",
       "      <td>1.105348</td>\n",
       "      <td>3910.261791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>726</td>\n",
       "      <td>603520.SH</td>\n",
       "      <td>20190902</td>\n",
       "      <td>司太立</td>\n",
       "      <td>0.091267</td>\n",
       "      <td>-0.006715</td>\n",
       "      <td>0.734482</td>\n",
       "      <td>18460.6960</td>\n",
       "      <td>1.545366</td>\n",
       "      <td>3692.585826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1197</td>\n",
       "      <td>603806.SH</td>\n",
       "      <td>20190902</td>\n",
       "      <td>福斯特</td>\n",
       "      <td>0.289425</td>\n",
       "      <td>0.020582</td>\n",
       "      <td>0.593928</td>\n",
       "      <td>14592.7600</td>\n",
       "      <td>1.292178</td>\n",
       "      <td>2918.986352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date  name   dif_dea  dif_dea_0  revenue_mean  \\\n",
       "1   3960  000519.SZ   20190902  中兵红箭  0.096063  -0.003129      0.708039   \n",
       "7   2890  601808.SH   20190902  中海油服  0.159121  -0.004853      0.786317   \n",
       "8   3960  000950.SZ   20190902  重药控股  0.053289  -0.005262      0.688062   \n",
       "11  3434  002025.SZ   20190902  航天电器  0.346147   0.019806      0.827409   \n",
       "19  2421  002294.SZ   20190902   信立泰  0.065282  -0.002448      0.658768   \n",
       "21  3960  001872.SZ   20190902  招商港口  0.139084   0.009176      0.733599   \n",
       "22  1024  002755.SZ   20190902   奥赛康  0.056974  -0.010211      0.695590   \n",
       "27   781  002801.SZ   20190902  微光股份  0.357187   0.030788      0.790432   \n",
       "28   726  603520.SH   20190902   司太立  0.091267  -0.006715      0.734482   \n",
       "30  1197  603806.SH   20190902   福斯特  0.289425   0.020582      0.593928   \n",
       "\n",
       "    vol_mean_20  vol_discount_mean   rank_factor  \n",
       "1   495468.7315           1.748179  99094.230960  \n",
       "7   102085.7880           1.528857  20417.629055  \n",
       "8    91999.8085           1.667091  18400.414314  \n",
       "11   70923.0915           0.968324  14185.039920  \n",
       "19   42178.3145           1.370126   8436.055325  \n",
       "21   34805.2205           1.381836   6961.472232  \n",
       "22   33454.3490           1.420239   6691.275278  \n",
       "27   19549.0750           1.105348   3910.261791  \n",
       "28   18460.6960           1.545366   3692.585826  \n",
       "30   14592.7600           1.292178   2918.986352  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dif_dea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dif_dea_plot = enter_util.enter_for_plot(test_dif_dea)\n",
    "enter_true, enter_false, enter_discount = plot_util.plot_enter(test_dif_dea_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enter_discount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对日k进行筛选，剔除dif，dea较大的数据(待定)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dif_dea_0 = test_dif_dea[test_dif_dea[\"dif_dea_0\"] < 0.022 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>stock_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dif_dea</th>\n",
       "      <th>dif_dea_0</th>\n",
       "      <th>revenue_mean</th>\n",
       "      <th>vol_mean_20</th>\n",
       "      <th>vol_discount_mean</th>\n",
       "      <th>rank_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3960</td>\n",
       "      <td>000519.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>中兵红箭</td>\n",
       "      <td>0.096063</td>\n",
       "      <td>-0.003129</td>\n",
       "      <td>0.708039</td>\n",
       "      <td>495468.7315</td>\n",
       "      <td>1.748179</td>\n",
       "      <td>99094.230960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2890</td>\n",
       "      <td>601808.SH</td>\n",
       "      <td>20190902</td>\n",
       "      <td>中海油服</td>\n",
       "      <td>0.159121</td>\n",
       "      <td>-0.004853</td>\n",
       "      <td>0.786317</td>\n",
       "      <td>102085.7880</td>\n",
       "      <td>1.528857</td>\n",
       "      <td>20417.629055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3960</td>\n",
       "      <td>000950.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>重药控股</td>\n",
       "      <td>0.053289</td>\n",
       "      <td>-0.005262</td>\n",
       "      <td>0.688062</td>\n",
       "      <td>91999.8085</td>\n",
       "      <td>1.667091</td>\n",
       "      <td>18400.414314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3434</td>\n",
       "      <td>002025.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>航天电器</td>\n",
       "      <td>0.346147</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>0.827409</td>\n",
       "      <td>70923.0915</td>\n",
       "      <td>0.968324</td>\n",
       "      <td>14185.039920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2421</td>\n",
       "      <td>002294.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>信立泰</td>\n",
       "      <td>0.065282</td>\n",
       "      <td>-0.002448</td>\n",
       "      <td>0.658768</td>\n",
       "      <td>42178.3145</td>\n",
       "      <td>1.370126</td>\n",
       "      <td>8436.055325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3960</td>\n",
       "      <td>001872.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>招商港口</td>\n",
       "      <td>0.139084</td>\n",
       "      <td>0.009176</td>\n",
       "      <td>0.733599</td>\n",
       "      <td>34805.2205</td>\n",
       "      <td>1.381836</td>\n",
       "      <td>6961.472232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1024</td>\n",
       "      <td>002755.SZ</td>\n",
       "      <td>20190902</td>\n",
       "      <td>奥赛康</td>\n",
       "      <td>0.056974</td>\n",
       "      <td>-0.010211</td>\n",
       "      <td>0.695590</td>\n",
       "      <td>33454.3490</td>\n",
       "      <td>1.420239</td>\n",
       "      <td>6691.275278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>726</td>\n",
       "      <td>603520.SH</td>\n",
       "      <td>20190902</td>\n",
       "      <td>司太立</td>\n",
       "      <td>0.091267</td>\n",
       "      <td>-0.006715</td>\n",
       "      <td>0.734482</td>\n",
       "      <td>18460.6960</td>\n",
       "      <td>1.545366</td>\n",
       "      <td>3692.585826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1197</td>\n",
       "      <td>603806.SH</td>\n",
       "      <td>20190902</td>\n",
       "      <td>福斯特</td>\n",
       "      <td>0.289425</td>\n",
       "      <td>0.020582</td>\n",
       "      <td>0.593928</td>\n",
       "      <td>14592.7600</td>\n",
       "      <td>1.292178</td>\n",
       "      <td>2918.986352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index stock_code trade_date  name   dif_dea  dif_dea_0  revenue_mean  \\\n",
       "1   3960  000519.SZ   20190902  中兵红箭  0.096063  -0.003129      0.708039   \n",
       "7   2890  601808.SH   20190902  中海油服  0.159121  -0.004853      0.786317   \n",
       "8   3960  000950.SZ   20190902  重药控股  0.053289  -0.005262      0.688062   \n",
       "11  3434  002025.SZ   20190902  航天电器  0.346147   0.019806      0.827409   \n",
       "19  2421  002294.SZ   20190902   信立泰  0.065282  -0.002448      0.658768   \n",
       "21  3960  001872.SZ   20190902  招商港口  0.139084   0.009176      0.733599   \n",
       "22  1024  002755.SZ   20190902   奥赛康  0.056974  -0.010211      0.695590   \n",
       "28   726  603520.SH   20190902   司太立  0.091267  -0.006715      0.734482   \n",
       "30  1197  603806.SH   20190902   福斯特  0.289425   0.020582      0.593928   \n",
       "\n",
       "    vol_mean_20  vol_discount_mean   rank_factor  \n",
       "1   495468.7315           1.748179  99094.230960  \n",
       "7   102085.7880           1.528857  20417.629055  \n",
       "8    91999.8085           1.667091  18400.414314  \n",
       "11   70923.0915           0.968324  14185.039920  \n",
       "19   42178.3145           1.370126   8436.055325  \n",
       "21   34805.2205           1.381836   6961.472232  \n",
       "22   33454.3490           1.420239   6691.275278  \n",
       "28   18460.6960           1.545366   3692.585826  \n",
       "30   14592.7600           1.292178   2918.986352  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dif_dea_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dif_dea_0_plot = enter_util.enter_for_plot(test_dif_dea_0)\n",
    "enter_true, enter_false, enter_discount = plot_util.plot_enter(test_dif_dea_0_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enter_discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
